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import copy
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.tools import tool
def format_search_docs(search_docs):
"""Format search documents into a consistent string format.
Args:
search_docs: List of document objects with metadata and page_content.
Returns:
Formatted string with document sources and content.
"""
return "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
]
)
@tool
def wiki_search(query: str) -> str:
"""Search Wikipedia for a query and return maximum 2 results.
Args:
query: The search query."""
search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
formatted_search_docs = format_search_docs(search_docs)
return {"wiki_results": formatted_search_docs}
@tool
def web_search(query: str) -> str:
"""Search Tavily for a query and return maximum 3 results.
Args:
query: The search query."""
search_docs = TavilySearchResults(max_results=3).invoke(query=query)
formatted_search_docs = format_search_docs(search_docs)
return {"web_results": formatted_search_docs}
@tool
def arxiv_search(query: str) -> str:
"""Search Arxiv for a query and return maximum 3 result.
Args:
query: The search query."""
search_docs = ArxivLoader(query=query, load_max_docs=3).load()
truncated_docs = []
for doc in search_docs:
doc_copy = copy.copy(doc)
doc_copy.page_content = doc.page_content[:1000]
truncated_docs.append(doc_copy)
formatted_search_docs = format_search_docs(truncated_docs)
return {"arxiv_results": formatted_search_docs}
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